فصلنامه مدیریت زنجیره تامین
پیاپی 75 (تابستان 1401)

Design and analysis of distribution systems are among the key factors which have been of interest to logistics corporations in recent years. Two main elements in designing a distribution network are finding acceptable locations for facilities and effective routes. Simultaneous consideration of these two elements is called location routing problem. Nowadays, because of environment pollutions, making good decisions about declining the CO2 emission rate has become a critical issue. The main contributor in CO2 emissions are fleet vehicles. This paper aims to propose a new mathematical model for the location routing problem in order to reduce the distribution and hence the fuel costs which in turn lead to CO2 emission rate reduction. Driver satisfaction is also pursued by balancing the drivers' workloads. A mathematical model is proposed for the problem and then linearized and validated for small scale conditions. As the large scale problem has many complexities, a multipurpose optimization algorithm, namely the NSGA-II algorithm which is a well-known metaheuristic algorithm is applied. To obtain a better solution, facility locations and route allocations are considered simultaneously. The algorithm performance is evaluated by introducing 4 indicators and the numerical results are reported. The results show that the suggested algorithm has the required efficiency to produced high quality parato solutions which are uniformly distributed in the problem's solution space.

The purpose of this research is to present a two-objective nonlinear integer programming model for optimization of a general commodities supply chain, minimizing the purchase costs and delivery delays and maximizing the resilience with uncertain constraints and parameters of the objective functions. To solve the model, the weights of relevant metrics and sub-criteria for producers are obtained through multi-criteria decision making. These metrics are used as the input data to the proposed mathematical model. Since this is classified as a hybrid optimization problem in the NP-hard problem family, NSGA-II and MOPSO multi-objective evolutionary algorithms are used to solve the proposed model. Comparing the results of the algorithm is done with the help of comparative indices. In this study, the probability constraint programming is used for the fuzzy to deterministic conversion of models. This method fits the LAM and UAM estimation models appropriately to the pessimistic-optimistic changes due to the differences in the decision makers' attitudes. Results of comparing these algorithms indicate that the genetic algorithm performs better in the optimistic view, whilst the MOPSO algorithm performs better in the pessimistic case. The results of NSGA-II and MOPSO algorithm for the designed sample problem show that NSGA-II algorithm performs better than MOPSO algorithm in different criteria. For instance, better performance regarding the solution time and the criterion of the distance from the ideal point, is observed in all problems.

The purpose of this study is to identify and explain the main factors measuring agility in the mining industry as the most important non-oil industry in the country. This research is an applied research in terms of purpose and a combined research in terms of method. The statistical population of the research in the qualitative and quantitative sections includes the experts, intellectuals and scholars of the country's mining industry. The methods of data collection are questionnaires and surveys. In the qualitative section, first, through finding related research articles by the meta-combination method, 124 sources are selected and 16 criteria for measuring agility are identified. These include communications (information technology), customer orientation, coordinated planning, staff skills development, process integration, customer satisfaction, flexibility, product quality, cost reduction, market sensitivity and responsiveness, innovation, new product launches, delivery speed, technology changes, training and learning and trust. With a survey of 20 experts in the country's mining industry by the fuzzy Delphi method, 8 main factors are extracted, validated and screened from the listed criteria, namely, the product quality, market responsiveness, delivery speed, flexibility, reduction costs, innovation, customer orientation, communications and information technology. These indicators are then evaluated and prioritized by the DANP method and the identified factors are classified into four levels and two dependent/independent groups using the interpretive structural modeling and micro-analysis. The findings show that customer orientation is an essential element of supply chain agility that reduces the costs and increases the innovation. Cost reduction and improved innovation in turn, affect flexibility, product quality and communications. Furthermore, flexibility, product quality and communications factors affect the responsiveness and the speed of delivery. Finally, the delivery speed and the market responsiveness lead to the desired supply chain agility.

As environmental policies are highlighted in the green supply chain, it plays an essential role in improving the supply chain capabilities. On the other hand, considering the development of technology and the Internet of Things intelligence and their potentials in goal achievement, it is important to investigate and study their applications in this field. This study aims to identify the most important criteria in selecting a green supplier and investigate the impact of the Internet of Things (IoT) on this selection. To achieve these objectives, the fuzzy cognitive map (FCM) method is used to identify the effective weights of each criterion and introduce the most effective criterion by examining the centrality. The fuzzy hierarchical analysis (FAHP) is used to investigate the role of IoT in the green supply chain. The results indicate that air pollution control is the most important criterion for evaluating the suppliers and also has the greatest impact on IoT.

Today, the efficiency of the supply chain is a prerequisite for the success of many economic enterprises. Performance evaluation is also an important issue in the supply chain management. The purpose of the current research is to evaluate the performance of the service supply chain using the supply chain operational reference (SCOR) model and the fuzzy prioritization technique. In this research, samples are obtained through the purposeful and non-probability snowball sampling methods and the opinions of 15 Atka Company experts. The process of conducting this research includes three stages. The first stage is identifying the effective factors in performance evaluation of the service supply chain based on the SCOR approach. The second and third stages are screening the identified factors using the fuzzy Delphi technique and prioritizing the factors using the fuzzy approach. In the prioritization of the measurement scales the non-linear optimization model is used. The results show that in this performance evaluation, the criteria of reliability, property, agility, flexibility, cost and responsiveness achieve the highest priorities in descending order.

With the accession of Covid-19 epidemic, a lot of pressure was put on the stability of the supply chain of health care centers. Therefore, the purpose of this study is to identify and design a model of factors affecting the stability of the supply chain of health care centers in the Covid-19 era using the new analytical approach of interpretive structural modelling (ISM). This research is an applied and descriptive research. The information is collected from library and field sources and validated through expert opinions. Then, the interpretive structural modelling approach is used to analyze the relationships between the identified factors and their rating. The research results indicate that 39 factors which are classified into 8 categories of supply chain processes (namely, capacity management, technology management, environment management, service delivery management, financial management, risk management, event management and customer relationship management) affected the stability of the supply chain of health care centers during the Covid-19 era. Also, the results of ISM ratings of these processes show a five-level model in which capacity management with the most impact occupies the first level and customer relationship management and event management with the least impact are in the fifth level. Finally, some suggestions for supply chain stability maintenance are presented to the managers of health care centers.